# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/dd7354ccfcf24d50f2a62294e623ccf0a033da2a/startegy%20test/5/MFI2/MFI2.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter import Config class Github_hamidreza07_freqai_strategy__MFI2__20241005_141958(IStrategy): """ Simple strategy based on MFI (Volume-weighted Strength) How to use it? > python3 ./freqtrade/main.py -s Github_hamidreza07_freqai_strategy__MFI2__20241005_141958 """ buy_params = { "buy_adx": 89.0, "buy_adx_enabled": False, "buy_bb_enabled": True, "buy_bb_gain": 0.09, "buy_dm_enabled": False, "buy_ema_enabled": False, "buy_fisher": -0.01, "buy_fisher_enabled": True, "buy_mfi": 3.1, "buy_mfi_enabled": False, "buy_neg_macd_enabled": False, "buy_rsi": 17.1, "buy_rsi_enabled": False, "buy_sar_enabled": False, } # buy_params = { # "buy_adx": 89.0, # "buy_adx_enabled": False, # "buy_bb_enabled": True, # "buy_bb_gain": 0.07, # "buy_dm_enabled": False, # "buy_ema_enabled": False, # "buy_fisher": 0.83, # "buy_fisher_enabled": False, # "buy_mfi": 3.1, # "buy_mfi_enabled": False, # "buy_neg_macd_enabled": True, # "buy_rsi": 17.1, # "buy_rsi_enabled": True, # "buy_sar_enabled": True, # } buy_rsi = DecimalParameter(0.1, 25, decimals=1, default=20, space="buy") buy_mfi = DecimalParameter(0.1, 10, decimals=1, default=2, space="buy") buy_adx = DecimalParameter(1, 99, decimals=0, default=1, space="buy") buy_fisher = DecimalParameter(-1, 1, decimals=2, default=0.18, space="buy") buy_bb_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.04, space="buy") buy_bb_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_neg_macd_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_ema_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_adx_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_dm_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_mfi_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_sar_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_fisher_enabled = CategoricalParameter([True, False], default=True, space="buy") sell_mfi = DecimalParameter(80, 100, decimals=0, default=95, space="sell") sell_hold_enabled = CategoricalParameter([True, False], default=True, space="sell") startup_candle_count = 20 # set common parameters minimal_roi = Config.minimal_roi trailing_stop = Config.trailing_stop trailing_stop_positive = Config.trailing_stop_positive trailing_stop_positive_offset = Config.trailing_stop_positive_offset trailing_only_offset_is_reached = Config.trailing_only_offset_is_reached stoploss = Config.stoploss timeframe = Config.timeframe process_only_new_candles = Config.process_only_new_candles use_exit_signal = Config.use_exit_signal exit_profit_only = Config.exit_profit_only ignore_roi_if_entry_signal = Config.ignore_roi_if_entry_signal order_types = Config.order_types def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ # ADX dataframe['adx'] = ta.ADX(dataframe) # Plus Directional Indicator / Movement dataframe['dm_plus'] = ta.PLUS_DM(dataframe) dataframe['di_plus'] = ta.PLUS_DI(dataframe) # Minus Directional Indicator / Movement dataframe['dm_minus'] = ta.MINUS_DM(dataframe) dataframe['di_minus'] = ta.MINUS_DI(dataframe) dataframe['dm_delta'] = dataframe['dm_plus'] - dataframe['dm_minus'] dataframe['di_delta'] = dataframe['di_plus'] - dataframe['di_minus'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) # EMA - Exponential Moving Average dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ conditions = [] # GUARDS AND TRENDS if self.buy_ema_enabled.value: conditions.append(dataframe['close'] < dataframe['ema5']) if self.buy_rsi_enabled.value: conditions.append(dataframe['rsi'] > self.buy_rsi.value) if self.buy_dm_enabled.value: conditions.append(dataframe['dm_delta'] > 0) if self.buy_mfi_enabled.value: conditions.append(dataframe['mfi'] <= self.buy_mfi.value) # only buy if close is below SAR if self.buy_sar_enabled.value: conditions.append(dataframe['close'] < dataframe['sar']) if self.buy_fisher_enabled.value: conditions.append(dataframe['fisher_rsi'] < self.buy_fisher.value) if self.buy_neg_macd_enabled.value: conditions.append(dataframe['macd'] < 0.0) # potential gain > goal if self.buy_bb_enabled.value: conditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ if self.sell_hold_enabled.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: # Exit long position if price is above sma(5) or MFI > 90 dataframe.loc[ ( (dataframe['close'] > dataframe['ema5']) & (dataframe['mfi'] > self.sell_mfi.value) ), 'sell'] = 1 return dataframe # +--------+---------+----------+--------------------------+--------------+-------------------------------+-----------------+--------------+-------------------------------+ # | Best | Epoch | Trades | Win Draw Loss Win% | Avg profit | Profit | Avg duration | Objective | Max Drawdown (Acct) | # |--------+---------+----------+--------------------------+--------------+-------------------------------+-----------------+--------------+-------------------------------| # | * Best | 2/50 | 41 | 24 0 17 58.5 | 0.42% | 1869.894 USDT (18.70%) | 0 days 15:31:00 | -1,869.89440 | 121.992 USDT (1.02%) | # | Best | 34/50 | 59 | 40 2 17 67.8 | 0.36% | 2321.240 USDT (23.21%) | 0 days 11:46:00 | -2,321.24006 | 12.920 USDT (0.11%) |